论文标题
SC-DEPTHV3：动态场景的强大自我监督的单眼深度估计
SC-DepthV3: Robust Self-supervised Monocular Depth Estimation for Dynamic Scenes
论文作者
论文摘要
自我监督的单眼深度估计在静态场景中显示出令人印象深刻的结果。它依赖于训练网络的多视图一致性假设，但是在动态对象区域和阻塞中违反了。因此，现有方法在动态场景中的准确性较差，并且估计的深度图在物体边界处有模糊，因为它们通常在其他训练视图中被遮住。在本文中，我们建议SC-DEPTHV3解决挑战。具体而言，我们引入了一个外部预验证的单眼深度估计模型，用于生成单像深度之前，即伪深度，我们提出了新的损失，以促进自我监督的训练。结果，即使在高度动态场景的单眼视频中训练时，我们的模型也可以预测锐利而准确的深度图。我们证明了我们的方法的性能明显优于六个具有挑战性的数据集上的先前方法，并为拟议的术语提供了详细的消融研究。源代码和数据将在https://github.com/jiawangbian/sc_depth_pl上发布
Self-supervised monocular depth estimation has shown impressive results in static scenes. It relies on the multi-view consistency assumption for training networks, however, that is violated in dynamic object regions and occlusions. Consequently, existing methods show poor accuracy in dynamic scenes, and the estimated depth map is blurred at object boundaries because they are usually occluded in other training views. In this paper, we propose SC-DepthV3 for addressing the challenges. Specifically, we introduce an external pretrained monocular depth estimation model for generating single-image depth prior, namely pseudo-depth, based on which we propose novel losses to boost self-supervised training. As a result, our model can predict sharp and accurate depth maps, even when training from monocular videos of highly-dynamic scenes. We demonstrate the significantly superior performance of our method over previous methods on six challenging datasets, and we provide detailed ablation studies for the proposed terms. Source code and data will be released at https://github.com/JiawangBian/sc_depth_pl
